Introduction

Bot Type Scores

Bot scores describe how much an account acts like a specific kind of bot. https://botometer.osome.iu.edu/faq

Methodology

  1. Randomly sample n accounts from Twitter API.
  2. Collect Botometer and BotometerLite bot likelihood scores.
  3. Calculate the correlation between scores.

Results

  1. BotometerLite is most similar to the Botometer fake follower and spammer scores with \(R^2\) values of 0.394 and 0.334 respectively. Hence, if Botometer scores are accurate, BotometerLite may be somewhat effective at identifying potential fake followers and spammers.

The pearson correlation matrix (\(R^2\) values are the square of the values of this matrix) also shows the scores are weakly correlated.

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Conclusion

References

Rauchfleisch, Adrian, and Jonas Kaiser. 2020. “The False Positive Problem of Automatic Bot Detection in Social Science Research.” Berkman Klein Center Research Publication, nos. 2020-3.

Yang, Kai-Cheng, Onur Varol, Pik-Mai Hui, and Filippo Menczer. 2020. “Scalable and Generalizable Social Bot Detection Through Data Selection.” In Proceedings of the Aaai Conference on Artificial Intelligence, 34:1096–1103. 01.